Effective space-weather prediction and mitigation requires accurate forecasting
of near-Earth solar-wind conditions. Numerical magnetohydrodynamic models of the solar
wind, driven by remote solar observations, are gaining skill at forecasting the large-scale
solar-wind features that give rise to near-Earth variations over days and weeks. There remains
a need for accurate short-term (hours to days) solar-wind forecasts, however. In this
study we investigate the analogue ensemble (AnEn), or “similar day”, approach that was
developed for atmospheric weather forecasting. The central premise of the AnEn is that
past variations that are analogous or similar to current conditions can be used to provide a
good estimate of future variations. By considering an ensemble of past analogues, the AnEn
forecast is inherently probabilistic and provides a measure of the forecast uncertainty. We
show that forecasts of solar-wind speed can be improved by considering both speed and
density when determining past analogues, whereas forecasts of the out-of-ecliptic magnetic
field [BN] are improved by also considering the in-ecliptic magnetic-field components. In
general, the best forecasts are found by considering only the previous 6 – 12 hours of observations.
Using these parameters, the AnEn provides a valuable probabilistic forecast for
solar-wind speed, density, and in-ecliptic magnetic field over lead times from a few hours
to around four days. For BN, which is central to space-weather disturbance, the AnEn only
provides a valuable forecast out to around six to seven hours. As the inherent predictability
of this parameter is low, this is still likely a marked improvement over other forecast methods.
We also investigate the use of the AnEn in forecasting geomagnetic indices Dst and Kp.
The AnEn provides a valuable probabilistic forecast of both indices out to around four days.
We outline a number of future improvements to AnEn forecasts of near-Earth solar-wind
and geomagnetic conditions.